Model Overview def model(hparams, X, past=None, scope='model', reuse=tf.AUTO_REUSE): 模型的输入信息分两种:X和past,X是语言模型的输入,past是已生成上文的状态,实作分四种情况: 训练时,X为一组训练数据[2],past为空。 条件生成初始阶段,X为条件语句,past为空 无条件生成初始阶段,X为[end],past为...
MODE=$1 #run or debug GPUS_PER_NODE=$2 #申请的卡数 TOKENIZER=$3 #中文jiebabpe,英文gpt2bpe MODEL_SIZE=$4 #0.125B, 0.35B, 1.3B, 3.6B 等等 MOE=$5 #专家数量 RT=$6 #路由类型 BATCH_SIZE=$7 #batch size TP=$8 #模型并行度 AC=$9 #激活检查点类型 ZERO=${10} #是否打开zero-1 S...
allocated 2971 MiB for model parameters batch_size B=16 * seq_len T=1024 * num_processes=8 and total_batch_size=1048576 => setting grad_accum_steps=8 created directory: log_gpt2_1558M allocating 40409 MiB for activations val loss 11.129390 allocating 2971 MiB for parameter gradients allocat...
allocated 2971 MiB for model parameters batch_size B=16 * seq_len T=1024 * num_processes=8 and total_batch_size=1048576 => setting grad_accum_steps=8 created directory: log_gpt2_1558M allocating 40409 MiB for activations val loss 11.129390 allocating 2971 MiB for parameter gradients allocatin...
) new_shape = x.size()[:-2] + (x.size(-2)*x.size(-1),) return x.view(*new_shape) def forward(self, x): x = self.c_attn(x) #new `x` shape - `[1,3,2304]` q, k, v = x.split(self.d_model, dim=2) q, k, v = self.split_heads(q),...
model=GPT2LMHeadModel.from_pretrained(model_name).to(device)### Resize the embedding layer to the desired size model.resize_token_embeddings(len(tokenizer),desired_embedding_size)model=model.to(device)## save tokenizer and model to harddisk ...
RuntimeError: Error(s) in loading state_dict for PyTorchBasedGPT2: size mismatch for transformer.h.0.attn.c_attn.weight: copying a param with shape torch.Size([768, 2304]) from checkpoint, the shape in current model is torch.Size([2304, 768])... 一、...
GPT2 import GPT2Model, GPT2Tokenizer # 初始化GPT-2模型 model = GPT2Model( vocab_size=30000, layer_size=32, block_size=1024, embedding_dropout=0.0, embedding_size=2560, num_attention_heads=32, attention_dropout=0.0, residual_dropout=0.0...
vocab_size, bias=False) ... GPT2LMHeadModel的主要输入为input_ids,past_key_values,labels,分别代表输入文本,模型维护的Q,V上下文信息,目标预测文本def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, labels: Optional[...
from GPT2 import GPT2Model # 初始化GPT-2模型 model = GPT2Model( vocab_size=30000, layer_size=32, block_size=1024, embedding_dropout=0.0, embedding_size=2560, num_attention_heads=32, attention_dropout=0.0, residual_dropout=0.0) # 读取CPM-LM模型参数(FP16) ...